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Updated: May 6, 2026

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
An immune-related seven-gene signature for predicting lymph node metastasis in breast cancer
Yun Hu1, Lanqiao Sun2, Jinhua Wang3
1Department of Breast Surgery, Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Nanjing, China.
Background:
Breast cancer (BC) is the leading malignant tumors among females worldwide, which serves as a common chronic disease with several acute postoperative complications, including upper limb edema, hemorrhage, flap necrosis, effusion and so on. A majority of BC patients have lymph node metastasis, suffering from a poor prognosis. The immune system has been reported to participate in regulating BC lymph node metastasis. This study aimed to search for immune-related biomarkers for predicting BC lymph node metastasis.
Methods:
1057 BC patients were acquired from The Cancer Genome Atlas (TCGA) database as the training dataset while 327 BC patients were obtained from GSE20685 as the validation dataset. We get 2,175 immune genes from four immune-related gene sets. We divided BC patients into lymph node positive and negative groups to identify immune-related lymph node-associated differentially expressed genes (DEGs) for functional enrichment analysis and protein-protein interaction (PPI) network. In order to predict BC lymph node metastasis, we established an immune-related signature and assessed its predictive accuracy. In addition, we applied qRT-PCR to investigate signature gene expressions between normal breast epithelium cells and breast cancer cells.
Results:
We identified 336 immune-related lymph node-associated DEGs, which were enriched in leukocyte migration, immunoglobulin complex and receptor ligand activity among GO analysis and cytokine-cytokine receptor interaction among KEGG analysis. With the aim of predicting BC lymph node metastasis, we established a seven-gene immune-related signature, consisting of F2R, IKZF2, NAB1, RFX5, S100B, S1PR2 and VEGFA. The immune-related signature was proven to be an independent predictive factor for BC lymph node metastasis in both TCGA and GSE20685 databases. Compared with normal breast epithelium cells, RFX5, VEGFA were upregulated in breast cancer cells, IKZF2, NAB1, S100B were downregulated in breast cancer cells while F2R, S1PR2 showed no significance.
Conclusion:
We established a seven-gene immune-related signature for predicting lymph node metastasis in BC, which might provide a novel sight for BC diagnosis and treatment.
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